Research within the agricultural community has shown that management of crop production may be optimized by taking into account spatial variations that often exist within a given farming field. For example, by varying the farming inputs applied to a field according to local conditions within the field, a farmer can optimize crop yield as a function of the inputs being applied while preventing or minimizing environmental damage. This management technique has become known as precision, site-specific, prescription or spatially-variable farming.
The management of a field using precision farming techniques requires the gathering and processing of data relating to site-specific characteristics of the field. Generally, site-specific input data is analyzed in real-time or off-line to generate a prescription map including desired application or control rates of a farming input. A control system reads data from the prescription map and generates a control signal which is applied to a variable-rate controller for applying a farming input to the field at a rate that varies as a function of the location. Variable-rate controllers may be mounted on agricultural vehicles with attached variable-rate applicators, and may be used to control application rates for applying seed, fertilizer, insecticide, herbicide or other inputs. The effect of the inputs may be analyzed by gathering site-specific yield and moisture content data and correlating this data with the farming inputs, thereby allowing a user to optimize the amounts and combinations of farming inputs applied to the field.
The spatially-variable characteristic data may be obtained by manual measuring, remote sensing, or sensing during field operations. Manual measurements typically involve taking a soil probe and analyzing the soil in a laboratory to determine nutrient data or soil condition data such as soil type or soil classification. Taking manual measurements, however, is labor intensive and, due to high sampling costs, provides only a limited number of data samples. Remote sensing may include taking aerial photographs or generating spectral images or maps from airborne or spaceborne multispectral sensors. Spectral data from remote sensing, however, is often difficult to correlate with a precise position in a field or with a specific quantifiable characteristic of the field. Both manual measurements and remote sensing require a user to conduct an airborne or ground-based survey of the field apart from normal field operations.
Spatially-variable characteristic data may also be acquired during normal field operations using appropriate sensors supported by a combine, tractor or other vehicle. A variety of characteristics may be sensed including soil properties (e.g., organic matter, fertility, nutrients, moisture content, compaction, topography or altitude), crop properties (e.g., height, moisture content or yield), and farming inputs applied to the field (e.g., fertilizers, herbicides, insecticides, seeds, cultural practices or tillage parameters and techniques used). Other spatially-variable characteristics may be manually sensed as a field is traversed (e.g., insect or weed infestation or landmarks). As these examples show, characteristics which correlate to a specific location include data related to local conditions of the field, farming inputs applied to the field, and crops harvested from the field.
Special problems exist, however, when site-specific farming equipment is used on more than one farm, or on multiple fields that are part of a single farm. For example, because it is desirable to maintain farming data associated with each field independently of farming data associated with any other field, it would be desirable to have a site-specific farming system capable of maintaining data from different fields independently (e.g., using different files or layers of data). In such a system, an operator of a vehicle may be required to manually select a field being worked from among various fields that have been defined in the site-specific farming system. Requiring manual selection of a particular field, however, increases the number of tasks that an operator must perform. Also, manual selection may cause errors to occur in selecting a field being worked. Such errors may result in incorrect prescription maps being selected, or in received farming data being stored in geo-referenced maps which do not correspond to the correct field.